A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition
Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers’ actions in a timely manner. Recently,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/23/7455 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849220452223811584 |
|---|---|
| author | Yuanyuan Tian Sen Lin Hejun Xu Guangchong Chen |
| author_facet | Yuanyuan Tian Sen Lin Hejun Xu Guangchong Chen |
| author_sort | Yuanyuan Tian |
| collection | DOAJ |
| description | Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers’ actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. Deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network’s ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers’ health and work efficiency. |
| format | Article |
| id | doaj-art-3f404010a05943d3a1461b0e3bfb3fee |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3f404010a05943d3a1461b0e3bfb3fee2024-12-13T16:31:35ZengMDPI AGSensors1424-82202024-11-012423745510.3390/s24237455A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action RecognitionYuanyuan Tian0Sen Lin1Hejun Xu2Guangchong Chen3School of Civil Engineering and Architecture, Wuyi University, Jiangmen 529020, ChinaSchool of Business, East China University of Science and Technology, Shanghai 200231, ChinaSchool of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaGlobally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers’ actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. Deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network’s ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers’ health and work efficiency.https://www.mdpi.com/1424-8220/24/23/7455construction workeraction recognition3D skeletondeep learning algorithm |
| spellingShingle | Yuanyuan Tian Sen Lin Hejun Xu Guangchong Chen A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition Sensors construction worker action recognition 3D skeleton deep learning algorithm |
| title | A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition |
| title_full | A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition |
| title_fullStr | A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition |
| title_full_unstemmed | A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition |
| title_short | A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition |
| title_sort | spatial temporal multi feature network stmf net for skeleton based construction worker action recognition |
| topic | construction worker action recognition 3D skeleton deep learning algorithm |
| url | https://www.mdpi.com/1424-8220/24/23/7455 |
| work_keys_str_mv | AT yuanyuantian aspatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT senlin aspatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT hejunxu aspatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT guangchongchen aspatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT yuanyuantian spatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT senlin spatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT hejunxu spatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition AT guangchongchen spatialtemporalmultifeaturenetworkstmfnetforskeletonbasedconstructionworkeractionrecognition |